13. / 41
13
• Mutagenic potency
• Carcinogenic potency
• Endocrine disruption
• Growth inhibition
• Aqueous solubility
N
NH
O
O
H
H
H
H H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
O
O
O
O
O
O
Cl
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
Br
Br O P
O
O Br
Br
O
Br
Br
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
N
S
N
N
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
O
N
O
O
H
H
H
O
O
H
H
N
O
O
Cl
Cl
Cl
H
H
H
H
H
H H
N
O
O
H
H
H
H
H
H
H H
H
N
O
O
H
H
H
H
H
H
H
N
H
N
O
O
N
O
O
H
H
H
H
H
H
H
H
N
CH3
O
O
H
N Cl
Cl
Cl
Cl
Cl
H3C
O O
O
O
O
O
H3C
CH3
CH2
O
HN
O
O
NH
CH3
HO
OH
CH3
N
O
O
CH3
N
N
H
N
H
H3C
N
H3C
H3C
NH
O
N
O
N
O
CH3
O N
NH2
O
CH3
Br
CH3
N
H3C
H
N
S
N
O
CH3
N
OH
CH3
CH3
N
N
N
CH3
H3C
H2N NH2
H
OH
O
HO
CH3
H
H
O
CH3
H
O
O
H3C H
H
H
O
H3C
S
CH3
O
H
H
O
CH3
CH3
O
O
HO
H3C
H
HO
F
H
O
H3C
NH2
O
N
HO
H
O
O
H
H
O
O
O
H3C
O
O
O
CH3
O
CH3
H
O
CH3
H
O
O
CH3
H
H
N
H
N O
H3C
O
O
O
25. / 41
24
N O
C
C
C
C
H
H
H
H
H
N O
C
C
C
C
H
H
H
H
H
(sum, mean or max)
+ attention
<latexit sha1_base64="WNEpfX6Bt3G9f3Toyi7bd0iGAgY=">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</latexit>
hi
0
@hi,
M
j2Ni
(hi, hi, eij)
1
A
34. / 41
33
N O
C
C
C
C
H
H
H
H
H
Dipole moment
Isotropic polarizability
HOMO energy
LUMO energy
Gap between HOMO and LUMO
Electronic spatial extent
Zero point vibrational energy
Internal energy at 0K
Internal energy at 298.15K
Enthalpy at 298.15K
Free energy at 298.15K
Heat capavity at 298.15K
Atomization energy at 0K
Atomization energy at 298.15K
Atomization enthalpy at 298.15K
Atomization free energy at 298.15K
Rotational constant A
Rotational constant B
Rotational constant C
ICML 2017 https://arxiv.org/abs/1704.01212 JCTC 2017 https://doi.org/10.1021/acs.jctc.7b00577
35. / 41
34
1st place: 10 GNNs (12-Layer Graphormer) + 8 ExpC*s (5-Layer ExpandingConv)
73 GNNs (11-Layer LiteGEMConv with Self-Supervised Pretraining)
20 GNNs (32-Layer GNN with Noisy Nodes)
Test MAE 0.1200 (eV)
2nd place:Test MAE 0.1204 (eV)
3rd place: Test MAE 0.1205 (eV)
Results: https://ogb.stanford.edu/kddcup2021/results/#awardees_pcqm4m
https://ogb.stanford.edu/kddcup2021/
36. / 41
35
• the number of immediate neighbors who are
“heavy” (non-hydrogen) atoms
• the valence minus the number of hydrogens
• the atomic number
• the atomic mass
• the atomic charge
• the number of attached hydrogens
• whether the atom is contained in at least one ring
• hydrogen-bond acceptor or not?
• hydrogen-bond donor or not?
• negatively ionizable or not?
• positively ionizable or not?
• aromatic or not?
• halogen or not?
Rogers and Hahn, JCIM (2005) https://doi.org/10.1021/ci100050t
Faber et al, JCTC (2017) https://doi.org/10.1021/acs.jctc.7b00577
37. / 41
36
Continuous-Filter Convolutions
(cfconv layers)
<latexit sha1_base64="flzLPrMsSS6k1am7yfKyC95kal4=">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</latexit>
dij
rbf(γ,μ)
#rbf
MLP
dim of
element-wise product
Gaussian
Smearing
<latexit sha1_base64="CP/gGUdLE1ahITrVyosFmAESNpw=">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</latexit>
xi
X
j2Ni
xj (exp( (dij µ)))
<latexit sha1_base64="sUht8Mvlx5JoWAMHabceBQWRmAs=">AAACi3ichVHLSsNAFL2Nr1qtrboR3BRLxVWZWFEpLooiuOzDPqAtJYnTOjQvkrRYQ3/ApRsXdaPgQvwAP8CNP+CinyAuK7hx4U0aEC3WGyZz5sw9d87MFXWZmRYhfR83MTk1PeOfDczNBxdC4cWlgqm1DInmJU3WjJIomFRmKs1bzJJpSTeooIgyLYrNA2e/2KaGyTT12OrotKoIDZXVmSRYSJUqomKfdWusFo6SOHEjMgp4D0TBi7QWfoQKnIAGErRAAQoqWIhlEMDErww8ENCRq4KNnIGIufsUuhBAbQuzKGYIyDbx38BV2WNVXDs1TVct4SkyDgOVEYiRF3JPBuSZPJBX8vlnLdut4Xjp4CwOtVSvhS5Wch//qhScLTj9Vo31bEEddl2vDL3rLuPcQhrq2+dXg1wyG7PXyS15Q/83pE+e8AZq+126y9Bsb4wfEb3gi2GD+N/tGAWFzTi/HU9ktqKpfa9VfliFNdjAfuxACo4gDXm3D5fQg2suyCW4JLc3TOV8nmYZfgR3+AUcEZMo</latexit>
xi
Schütt, Kindermans, Sauceda, Chmiela, Tkatchenko, Müller,
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. https://arxiv.org/abs/1706.08566
38. / 41
37
Schütt et al, SchNet. (2017) https://arxiv.org/abs/1706.08566
Satorras et al, E(n) Equivariant Graph Neural Networks. (2021) https://arxiv.org/abs/2102.09844
Anderson et al, Cormorant. (2019) https://arxiv.org/abs/1906.04015
Unke et al, PhysNet. (2019) https://arxiv.org/abs/1902.08408
Klicpera et al, DimeNet++. (2020) https://arxiv.org/abs/2011.14115
Fuchs et al, SE(3)-Transformers. (2021) https://arxiv.org/abs/2006.10503
Köhler et al, Equivariant Flows (Radial Field). (2020) https://arxiv.org/abs/2006.02425
Thomas et al, Tensor Field Networks. (2018) https://arxiv.org/abs/1802.08219
<latexit sha1_base64="NH4UQ68bqmsH0AzQM//vHVYIu40=">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</latexit>
f(g · x) = g · f(x)
<latexit sha1_base64="z65vGkIR8AznuZeRro+w9TcH+xY=">AAACl3ichVHLSsNAFD3Gd3206kZxEyyVuilTFRVBLAri0qqtQislSac1mCYhmZZq8Qf8AReCoCAqfoAf4MYfcOEniMsKblx4mwZEi3rDZM6cuefOmbmqbeiuYOy5TWrv6Ozq7ukN9PUPDAZDQ8Np1yo7Gk9plmE5u6rickM3eUrowuC7tsOVkmrwHfVgtbG/U+GOq1vmtji0+V5JKZp6QdcUQVQuNFyIFrNa3hJydUpekgvR6lQuFGYx5oXcCuI+CMOPDSt0jyzysKChjBI4TAjCBhS49GUQB4NN3B5qxDmEdG+f4xgB0pYpi1OGQuwB/Yu0yvisSetGTddTa3SKQcMhpYwIe2K3rM4e2R17YR+/1qp5NRpeDmlWm1pu54Ino1vv/6pKNAvsf6n+9CxQwILnVSfvtsc0bqE19ZWj0/rW4makNsku2Sv5v2DP7IFuYFbetKsk3zz7w49KXujFqEHxn+1oBenpWHwuNpOcDSdW/Fb1YBwTiFI/5pHAOjaQovpVnOMaN9KYtCytSevNVKnN14zgW0jJT5AOlcI=</latexit>
f(g · x) = f(x)
<latexit sha1_base64="h5Nu57LzNEgsKVoc7SjFgDOfXxQ=">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</latexit>
g 2 G
<latexit sha1_base64="98i0QCpyFbQuYbP7ylFU0FuKpWo=">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</latexit>
f : X ! Y
40. / 41
39
NeurIPS 2020 ICML 2020, 2021
ICLR 2020, 2021
• Self-Supervised Graph Transformer on Large-Scale Molecular
Data
• RetroXpert: Decompose Retrosynthesis Prediction Like A
Chemist
• Reinforced Molecular Optimization with Neighborhood-
Controlled Grammars
• Autofocused Oracles for Model-based Design
• Barking Up the Right Tree: an Approach to Search over Molecule
Synthesis DAGs
• On the Equivalence of Molecular Graph Convolution and
Molecular Wave Function with Poor Basis Set
• CogMol: Target-Specific and Selective Drug Design for
COVID-19 Using Deep Generative Models
• A Graph to Graphs Framework for Retrosynthesis Prediction
• Hierarchical Generation of Molecular Graphs using Structural Motifs
• Learning to Navigate in Synthetically Accessible Chemical Space Using
Reinforcement Learning
• Reinforcement Learning for Molecular Design Guided by Quantum
Mechanics
• Multi-Objective Molecule Generation using Interpretable Substructures
• Improving Molecular Design by Stochastic Iterative Target
Augmentation
• A Generative Model for Molecular Distance Geometry
• GraphDF: A Discrete Flow Model for Molecular Graph Generation
• An End-to-End Framework for Molecular Conformation Generation via
Bilevel Programming
• Equivariant message passing for the prediction of tensorial properties
and molecular spectra
• Learning Gradient Fields for Molecular Conformation Generation
• Self-Improved Retrosynthetic Planning
• Directional Message Passing for Molecular Graphs
• GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
• Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
• A Fair Comparison of Graph Neural Networks for Graph Classification
• MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
• Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design
• Learning Neural Generative Dynamics for Molecular Conformation Generation
• Conformation-Guided Molecular Representation with Hamiltonian Neural Networks
• Symmetry-Aware Actor-Critic for 3D Molecular Design